CN114896226A - Asset management method and device based on cloud platform and related medium - Google Patents
Asset management method and device based on cloud platform and related medium Download PDFInfo
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Abstract
The invention discloses an asset management method, an asset management device and a related medium of a cloud platform, wherein the method comprises the following steps: acquiring asset data, and storing the asset data in a preset database based on a cloud platform technology, wherein the asset data comprises equity asset data and real estate asset data; respectively carrying out full-period digital management on the equity asset data and the real estate asset data in the database; and based on the full-period digital management result, storing and backing up the asset data by using a data migration technology. The invention can effectively improve the management efficiency and the safety performance of the asset data and reduce the management cost by carrying out the full-period digital management on the asset data and storing and backing up the asset data.
Description
Technical Field
The invention relates to the technical field of asset management, in particular to an asset management method and device based on a cloud platform and a related medium.
Background
Currently, with the deep fusion and reform of each company, the owned asset data scale is gradually enlarged, the asset data types are increasingly complicated, and in the prior art, for the management of asset data, a set of appropriate management platform is usually established for a certain type of asset data. It is obvious that, with the expansion of asset data, the management platform required to be built rises, which leads to the reduction of the overall management efficiency and the increase of the management cost, and moreover, the management platforms lack the cooperative cooperation, which complicates the communication between different asset data. And, because there are a plurality of management platforms, when managing, some asset data are inevitably neglected, and the security of all asset data cannot be guaranteed.
Disclosure of Invention
The embodiment of the invention provides an asset management method and device of a cloud platform, computer equipment and a storage medium, and aims to improve the management efficiency and the safety performance of asset data and reduce the management cost.
In a first aspect, an embodiment of the present invention provides an asset management method based on a cloud platform, including:
acquiring asset data, and storing the asset data in a preset database based on a cloud platform technology, wherein the asset data comprises equity asset data and real estate asset data;
respectively carrying out full-period digital management on the equity asset data and the real estate asset data in the database;
and based on the full-period digital management result, storing and backing up the asset data by using a data migration technology.
In a second aspect, an embodiment of the present invention provides an asset management device based on a cloud platform, including:
the data storage unit is used for acquiring asset data and storing the asset data in a preset database based on a cloud platform technology, wherein the asset data comprises stock right asset data and real estate asset data;
the data management unit is used for respectively carrying out full-period digital management on the equity asset data and the real estate asset data in the database;
and the data backup unit is used for storing and backing up the asset data by using a data migration technology based on the full-period digital management result.
In a third aspect, an embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor, when executing the computer program, implements the cloud platform-based asset management method according to the first aspect.
In a fourth aspect, the embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when executed by a processor, the computer program implements the cloud platform-based asset management method according to the first aspect.
The embodiment of the invention provides an asset management method and device of a cloud platform, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring asset data, and storing the asset data in a preset database based on a cloud platform technology, wherein the asset data comprises equity asset data and real estate asset data; respectively carrying out full-period digital management on the equity asset data and the real estate asset data in the database; and based on the full-period digital management result, storing and backing up the asset data by using a data migration technology. The embodiment of the invention can effectively improve the management efficiency and the safety performance of the asset data and reduce the management cost by carrying out the full-period digital management on the asset data and storing and backing up the asset data.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of an asset management method based on a cloud platform according to an embodiment of the present invention;
fig. 2 is a schematic view of a first sub-flow in an asset management method based on a cloud platform according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a second sub-flow in an asset management method based on a cloud platform according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a third sub-flow in an asset management method based on a cloud platform according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a fourth sub-flow in an asset management method based on a cloud platform according to an embodiment of the present invention;
fig. 6 is a schematic view of a fifth sub-flow in an asset management method based on a cloud platform according to an embodiment of the present invention;
fig. 7 is a schematic diagram illustrating a sixth sub-flow in an asset management method based on a cloud platform according to an embodiment of the present invention;
FIG. 8 is a schematic block diagram of an asset management device based on a cloud platform according to an embodiment of the present invention;
fig. 9 is a first sub-schematic block diagram of an asset management device based on a cloud platform according to an embodiment of the present invention;
FIG. 10 is a second sub-schematic block diagram of an asset management device based on a cloud platform according to an embodiment of the present invention;
FIG. 11 is a third sub-schematic block diagram of an asset management device based on a cloud platform according to an embodiment of the present invention;
FIG. 12 is a fourth sub-schematic block diagram of an asset management device based on a cloud platform according to an embodiment of the present invention;
fig. 13 is a fifth sub-schematic block diagram of an asset management device based on a cloud platform according to an embodiment of the present invention;
fig. 14 is a sixth sub-schematic block diagram of an asset management device based on a cloud platform according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, fig. 1 is a schematic flowchart of an asset management method based on a cloud platform according to an embodiment of the present invention, which specifically includes: steps S101 to S103.
S101, acquiring asset data, and storing the asset data in a preset database based on a cloud platform technology, wherein the asset data comprises equity asset data and real estate asset data;
s102, respectively carrying out full-period digital management on the equity asset data and the real estate asset data in the database;
and S103, storing and backing up the asset data by using a data migration technology based on the full-period digital management result.
In this embodiment, the asset data is first stored in a preset database, and then is subjected to full-cycle digital management in the database according to the type of the asset data, for example, full-cycle digital management on the equity asset data. And for the asset data after management updating, storage backup is carried out through a data migration technology so as to ensure the safety of the asset data. According to the embodiment, the asset data is subjected to full-period digital management and is stored and backed up, so that the management efficiency and the safety performance of the asset data can be effectively improved, and the management cost is reduced. The real estate asset data management method comprises the steps of carrying out full-period digital management on the equity asset data, specifically carrying out management before investment, management in investment, management after investment, quit management, wind control management and the like on the equity asset data, and carrying out management, balance management, asset management and the like on the real estate asset data when carrying out full-period digital management on the real estate asset data.
In a specific application scenario, the asset management method provided by the embodiment can realize common services of functions such as main data, leasing, finance, auxiliary decision, statistical analysis report forms and land, that is, the functions of the main data, the leasing, the finance, the auxiliary decision, the statistical analysis report forms and the land are unified and operated in one platform, and all services use one set of service logic. For special applications including recruiting, operation, marketing and the like, the modules are closely connected with the sharing function, and the modules are not required to be connected with the financial sharing center and can be relatively independent. Meanwhile, in order to meet the requirements of complex and changeable customers and adapt to the mobile internet era of high-speed development, the asset management method can also realize interconnection and intercommunication with other business systems of a company through a standardized interface.
In one embodiment, as shown in fig. 2, the step S101 includes: steps S201 to S205.
S201, sending a remote procedure call protocol request to an HBase database;
s202, receiving meta-information sent by the HBase database according to the remote procedure call protocol request, and acquiring the number of pre-partitions according to the meta-information;
s203, sequentially sequencing each partition data in an ascending order according to the column keys and the row keys to obtain the corresponding sequenced partition data;
s204, inputting the sorted partition data into a local HDFS layer to convert the sorted partition data into corresponding asset data;
s205, sending the asset data to a partition server corresponding to the Hbase database for storage.
In this embodiment, an RPC request (an RPC request, that is, a remote procedure call protocol request, which is a request service from a remote computer program through a network) is initiated by a cloud platform technology (for example, a cloud computing platform), zk meta information of the Hbase database (that is, ZooKeeper meta information, ZooKeeper being a distributed application program coordination service that is a distributed and open source code) is accessed, partition information of a table pre-established by the Hbase is already stored in the zk meta information, and thus, the number of pre-partitions in the Hbase database can be known. By knowing the number of pre-partitions in the HBase database, the full data can be accurately divided into the same number of partitions. The HBase database is a distributed open source database, each pre-partition in the HBase database corresponds to one partition server, the HBase database is a distributed storage system which is high in reliability, high in performance, column-oriented and telescopic and based on Hadoop, and a large-scale structured storage cluster can be built on a cheap computer server by utilizing the HBase technology.
Since each pre-partition in the HBase database corresponds to one partition server, and each partition data uniquely corresponds to one partition server, the corresponding relationship between the partition data and the partition server may be a preset corresponding relationship, for example, partition 1 corresponds to partition server 1, … …, and partition N corresponds to partition server N. After the corresponding relation between each partition data and the partition server is obtained, directional storage can be realized when data storage is subsequently carried out, and the storage efficiency is improved.
After the full data are partitioned according to the number of pre-partitioned areas, sequencing each partitioned data, and sending the sequenced data to the Hbase database to be quickly stored. At this time, when sorting the partition data, the sizes of the column value and the row key value may be selected for sorting.
After sequencing of the partition data is completed to obtain the corresponding sequencing partition data, the asset data is directly sent to the Hbase database for storage, and the storage efficiency is guaranteed.
In one embodiment, as shown in fig. 3, the step S102 includes: steps S301 to S303.
S301, respectively constructing an investment enterprise management platform and a real estate management platform according to the partition servers of the Hbase database;
s302, performing pre-investment management, mid-investment management, post-investment management, quit management and wind control management on the equity asset data by using the investment enterprise management platform; and
s303, performing operation management, balance management and asset management on the real estate asset data by using the real estate management platform.
Since the asset data includes different types of data, such as equity asset data, real estate asset data, and the like, the different types of asset data may be stored in different partition servers, respectively, when stored. The stored asset data is then managed within each partitioned server. For example, in the present embodiment, the equity asset data and the real estate asset data are stored to different partition servers (e.g., an equity asset partition server and a real estate asset partition server), and then the equity asset data is managed within the equity asset partition servers, and the real estate asset data is managed within the real estate asset partition servers. It should be clear that the investment enterprise management platform described in this embodiment may be understood as a shareholder asset partition server itself, or may be a management platform built in the partition server, and the real estate management platform may be understood as a real estate asset partition server itself, or may be a management platform built in the partition server.
In a specific embodiment, when the investment enterprise management platform is used for performing pre-investment management on the equity asset data, a reserve item corresponding to the equity asset data may be obtained first, and project establishment is performed according to the reserve item, and then project optimization and project decision making are performed. When the investment enterprise management platform is used for carrying out investment management on the equity asset data, an investment agreement can be signed firstly, then an investment instruction is obtained, and investment cost, a guarantee fund and the like are set at the same time. When the investment enterprise management platform is used for post-investment management of the equity asset data, on one hand, enterprise management operations, namely organization management of board parties, prisoners and the like, business management of board parties, prisoners and the like, management of three parties (board parties, prisoners, stockholders) and the like can be executed; on the other hand, enterprise business operations, namely key work/KPI management, data reporting, post-delivery evaluation and the like can be executed. When the investment enterprise management platform is used for carrying out exit management on the equity asset data, an exit scheme can be made, and then the exit decision, the exit summary, the information registration and other items can be carried out. When the investment enterprise management platform is used for carrying out wind control management on the equity asset data, project progress can be checked, risk reports can be confirmed, important matters can be confirmed, regular evaluation can be carried out, and the like.
When the real estate management platform is used for carrying out operation management on the real estate asset data, the operation management can be realized from three aspects of property management (such as material warehouse, security management, quality management, energy consumption management and the like), leasing operation (such as recruitment management, entrusting management and the like) and assorted service multiple operations (such as information service, life service and the like). In the payment management, it is possible to separately perform income management (e.g., bill management, receivable management, etc.) and expenditure management (e.g., bill payable, refund payment, etc.). When the asset management is carried out, the management can be carried out from the aspects of asset accounts, asset ownership, asset archives, value evaluation, asset cards and the like.
In one embodiment, as shown in fig. 4, the step S302 includes: steps S401 to S404.
S401, acquiring an investment instruction for the equity asset data, and selecting at least one corresponding risk control index according to elements of the investment instruction;
s402, splicing the elements of the investment instruction into key values according to each risk control index, and matching a computing node for the risk control index according to the key values;
s403, acquiring a position holding value of a computing node, and accumulating the position holding value and a computing field of the risk control index to obtain a computing result of the risk control index;
s404, summarizing the calculation results of the risk control indexes, and taking the summarized results as the wind control management standard of the investment instruction.
In this embodiment, when the equity asset data is managed, especially when wind control management is performed, risk management and control may be performed on the corresponding investment instruction to determine whether the investment instruction should be issued and executed. Specifically, at least one risk control index is determined according to an investment instruction, then, for each risk control index, elements (such as time, amount of money and the like) of the investment instruction are spliced into a key value (namely a key value), and here, because a plurality of risk control indexes may exist, different computing nodes are allocated to the risk control indexes, so that each risk control index can be performed simultaneously, and the management efficiency is improved. And then, aiming at different calculation nodes, calculating the position taken value of the calculation node, and accumulating the position taken value with the calculation field of the risk control index, wherein the accumulated result is the calculation result of the risk control index. And finally, summarizing the calculation results of all risk control indexes, wherein the summarized result is the wind control management standard of the investment instruction. Of course, the wind management criteria described herein may also be understood as a risk reference or opinion to the investment order.
In an embodiment, as shown in fig. 4, the cloud platform-based asset management method further includes: steps S501 to S503.
S501, taking the calculation result of the risk control index as sample data, setting the number of input layer nodes of a BP neural network model according to the number of the sample data, and setting the number of output layer nodes of the BP neural network model as one;
s502, determining the number of hidden layer nodes of the BP neural network model by adopting a trial and error method according to the number of the input layer nodes and the number of the output layer nodes;
s503, inputting the sample data into an input layer using the BP neural network model, completing the learning of the sample data through a hidden layer and an output layer in sequence, and taking the output result of the output layer as the evaluation result of the sample data.
In this embodiment, an evaluation model (i.e., the BP neural network model) is established to evaluate a calculation result of a risk control index (i.e., the wind control management standard), so as to ensure that the obtained calculation result is accurate and reliable, thereby implementing fine management on equity asset data. Specifically, the number of nodes of an input layer of an evaluation model is set according to sample data, then the number of nodes of a hidden layer is determined through a trial and error method (namely, continuous trial and error), then the sample data is input into the input layer and sequentially passes through the hidden layer and an output layer, and a final evaluation result is obtained.
In an embodiment, as shown in fig. 6, the step S303 includes: steps S601 to S604.
S601, selecting corresponding risk evaluation indexes according to real estate information corresponding to the real estate asset data;
s602, constructing an evaluation index judgment matrix according to the risk evaluation index, and carrying out consistency check on the evaluation index judgment matrix;
s603, calculating a weight vector of the risk evaluation index through the evaluation index judgment matrix;
s604, calculating the risk integral of the real estate asset data by using the weight vector, and taking the risk integral as the value evaluation standard of the real estate asset data to finish asset management.
In this embodiment, when real estate asset data is managed, particularly when asset management is performed, risk evaluation may be performed on the real estate asset data, that is, value evaluation may be performed on the real estate asset data, so as to ensure that a corresponding risk reference suggestion exists when real estate is handled. Specifically, a corresponding risk evaluation index is selected according to the information of the real estate asset data, then an evaluation index judgment matrix is constructed according to the risk evaluation index, and consistency inspection is carried out. Here, the consistency check is performed in order to make the comparison of the importance of the influencing factors logically consistent, and if the consistency ratio is less than 0.1, the check is passed; if not, the judgment matrix needs to be reconstructed. After the consistency check is completed, the evaluation index judgment matrix is used for calculating the risk evaluation index to obtain a corresponding weight vector, then the weight vector is used for calculating the risk integral of the real estate asset data, and the risk integral is used as the value evaluation standard (or risk reference suggestion) of the real estate asset data.
In a specific implementation scenario, an evaluation index judgment matrix is constructed by adopting an analytic hierarchy process, and has the following properties:
in the formula, a ij The result of comparing the importance of the element i and the element j representing the risk evaluation index. a is ji The result of comparing the importance of the element j and the element i representing the risk evaluation index.
Further, the consistency test is carried out on the evaluation index judgment matrix according to the following formula:
in the formula, CI represents a consistency index calculation result, and the smaller CI is, the greater the consistency is, and vice versa; λ represents the maximum characteristic root of the evaluation index judgment matrix, and n represents an n-order matrix.
Next, the weight vector is calculated according to the following formula:
in the formula, W j Weight vector representing jth feature vector in evaluation index decision matrix, B j And expressing the jth characteristic vector in the evaluation index judgment matrix.
In one embodiment, as shown in fig. 7, the step S103 includes: steps S701 to S703.
S701, receiving a data migration instruction, and performing accuracy test and verification test on asset data to be migrated according to the data migration instruction;
s702, after the accuracy test and the verification test are finished, packaging the asset data into a data stream;
and S703, transmitting the data stream to a target server in real time through protocol service, and analyzing the data stream by the target server to obtain the asset data to be migrated.
In this embodiment, the asset data in the database is automatically migrated to the next-level mass storage device according to a specified policy by performing data migration on the asset data. When the asset data is needed, the hierarchical storage system will automatically recall the asset data from the next level of mass storage device back to the database.
When data migration is performed, the preliminary investigation is an important step and is also a crucial part. Before data migration work is carried out, related documents of an old system, such as a data dictionary, an operation manual and the like, are read to be familiar with the original system. The method is used as a common user to practice operation on the basis of reading documents, and the service flow of the old system is deeply known. In other words, the previous investigation is to make a comprehensive understanding of the old system, for example, the service scope of the old system, several sets of service systems and their relationship exist; developers, development tools, development platforms and adopted databases of the old system; key data distribution conditions of old systems: including data range, data size, etc.; business process of the old system; a data structure of the old system; old system organization architecture, project overview old system function usage, and the like.
And after the preliminary investigation is completed, the asset data is sorted and analyzed. The main work of data sorting and analysis is to comb out key business tables, table fields, table structures and table association conditions of the old system through various forms or tools; and screening out effective data and table fields, taking the new system as a reference, and corresponding the data dictionary of the old system to the data dictionary of the new system. Data fields needing to be converted in the data comparison table are in one-to-one correspondence, the names, types, precision and the like of the fields in the new data table and the old data table are described in detail, meanwhile, the data conversion mode is clarified, and a data comparison table is formed as a technical scheme for data migration.
And (5) converting the sorted and analyzed data according to the requirements of the comparison table, and writing the data into a new system. Data migration mainly adopts background or foreground import, wherein the background import form may involve technical means such as intermediate library, batch processing, affairs and the like. Before data conversion of the service system, a time point needs to be selected for data acquisition of the service system.
In addition, data integrity and accuracy are critical to commissioning. And the data of the new system is ensured to be consistent with the data of the old system by taking the visible data of the foreground interface as the standard, such as data of resource items, total resource area, arrearage amount and the like.
After the data is checked and confirmed, a service test can be carried out to check whether the data has association and interaction. For example, the rental resource status and contract execution status, whether the contract payment plan and the finance account receivable are consistent; the contract is associated with whether or not a deposit exists.
Fig. 8 is a schematic block diagram of an asset management apparatus 800 based on a cloud platform according to an embodiment of the present invention, where the apparatus 800 includes:
the data storage unit 801 is configured to acquire asset data and store the asset data in a preset database based on a cloud platform technology, where the asset data includes equity asset data and real estate asset data;
a data management unit 802, configured to perform full-cycle digital management on the equity asset data and the real property asset data in the database, respectively;
and the data backup unit 803 is used for storing and backing up the asset data by using a data migration technology based on the full-period digital management result.
In one embodiment, as shown in fig. 9, the data storage unit 801 includes:
a request sending unit 901, configured to send a remote procedure call protocol request to the HBase database;
a meta-information sending unit 902, configured to receive meta-information sent by the HBase database according to the remote procedure call protocol request, and obtain the number of pre-partitions according to the meta-information;
an ascending sorting unit 903, configured to sequentially perform ascending sorting on each partition data according to the column and row keys to obtain corresponding sorted partition data;
a data conversion unit 904, configured to input each sorted partition data into a local HDFS layer, so as to convert each sorted partition data into corresponding asset data;
and the server storage unit 905 is configured to send the asset data to a partition server corresponding to the Hbase database for storage.
In one embodiment, as shown in fig. 10, the data management unit 802 includes:
a platform construction unit 1001, configured to respectively construct an investment enterprise management platform and a real estate management platform according to the partition server of the Hbase database;
the equity management unit 1002 is used for performing pre-investment management, mid-investment management, post-investment management, quit management and wind control management on the equity asset data by using the investment enterprise management platform; and
the real estate management unit 1003 is configured to perform operation management, balance management, and asset management on the real estate asset data by using the real estate management platform.
In one embodiment, as shown in FIG. 11, the illustrated equity management unit 1002 includes:
an instruction obtaining unit 1101, configured to obtain an investment instruction for the equity asset data, and select at least one corresponding risk control indicator according to an element of the investment instruction;
the element splicing unit 1102 is configured to splice elements of the investment instruction into a key value for each risk control indicator, and match a computing node for the risk control indicator according to the key value;
a field accumulation unit 1103, configured to obtain a position taken value of a computing node, and accumulate the position taken value and a computing field of the risk control indicator to obtain a computing result of the risk control indicator;
and a result summarizing unit 1104, configured to summarize calculation results of the risk control indexes, and use the summarized results as a wind control management standard of the investment instruction.
In one embodiment, as shown in fig. 12, the cloud platform based asset management device 800 further includes:
a first node setting unit 1201, configured to set a calculation result of the risk control index as sample data, set an input layer node number of the BP neural network model according to the number of the sample data, and set an output layer node number of the BP neural network model as one;
a second node setting unit 1202, configured to determine, according to the number of nodes in the input layer and the number of nodes in the output layer, the number of nodes in the hidden layer of the BP neural network model by using a trial and error method;
and a data learning unit 1203, configured to input the sample data into an input layer that utilizes the BP neural network model, complete learning of the sample data through a hidden layer and an output layer in sequence, and use an output result of the output layer as an evaluation result of the sample data.
In one embodiment, as shown in fig. 13, the real estate management unit 1003 comprises:
an index selection unit 1301, configured to select a corresponding risk evaluation index according to the real estate information corresponding to the real estate asset data;
a matrix construction unit 1302, configured to construct an evaluation index determination matrix according to the risk evaluation index, and perform consistency check on the evaluation index determination matrix;
a vector calculation unit 1303 configured to calculate a weight vector of the risk evaluation index from the evaluation index determination matrix;
and a standard setting unit 1304, configured to calculate a risk score of the real estate asset data by using the weight vector, and use the risk score as a value evaluation standard of the real estate asset data, so as to complete asset management.
In one embodiment, as shown in fig. 14, the data backup unit 803 includes:
the data testing unit 1401 is used for receiving a data migration instruction, and performing accuracy testing and verification testing on the asset data to be migrated according to the data migration instruction;
a data packaging unit 1402, configured to package the asset data into a data stream after the accuracy test and the verification test are completed;
a data parsing unit 1403, configured to transmit the data stream to a target server in real time through a protocol service, and parse the data stream by the target server to obtain asset data to be migrated.
Since the embodiments of the apparatus portion and the method portion correspond to each other, please refer to the description of the embodiments of the method portion for the embodiments of the apparatus portion, which is not repeated here.
Embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed, the steps provided by the above embodiments can be implemented. The storage medium may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The embodiment of the present invention further provides a computer device, which may include a memory and a processor, where the memory stores a computer program, and the processor may implement the steps provided in the above embodiments when calling the computer program in the memory. Of course, the computer device may also include various network interfaces, power supplies, and the like.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. An asset management method based on a cloud platform is characterized by comprising the following steps:
acquiring asset data, and storing the asset data in a preset database based on a cloud platform technology, wherein the asset data comprises equity asset data and real estate asset data;
respectively carrying out full-period digital management on the equity asset data and the real estate asset data in the database;
and based on the full-period digital management result, storing and backing up the asset data by using a data migration technology.
2. The cloud platform-based asset management method according to claim 1, wherein the acquiring asset data and storing the asset data in a preset database based on a cloud platform technology comprises:
sending a remote procedure call protocol request to an HBase database;
receiving meta-information sent by the HBase database according to the remote procedure call protocol request, and acquiring the number of pre-partitions according to the meta-information;
sequentially sorting each partition data in an ascending order according to the column keys and the row keys to obtain corresponding sorted partition data;
inputting each sorted partition data into a local HDFS layer so as to convert each sorted partition data into corresponding asset data;
and sending the asset data to a partition server corresponding to the Hbase database for storage.
3. The cloud platform-based asset management method according to claim 2, wherein the performing full-cycle digital management on the equity asset data and the real property asset data in the database respectively comprises:
respectively constructing an investment enterprise management platform and a real estate management platform according to the partition server of the Hbase database;
the investment enterprise management platform is used for carrying out pre-investment management, mid-investment management, post-investment management, quit management and wind control management on the equity asset data; and
and performing operation management, balance management and asset management on the real estate asset data by using the real estate management platform.
4. The cloud platform-based asset management method according to claim 3, wherein the pre-investment management, mid-investment management, post-investment management, quit management and wind control management of the equity asset data by using the investment enterprise management platform comprises:
acquiring an investment instruction for the equity asset data, and selecting at least one corresponding risk control index according to elements of the investment instruction;
splicing the elements of the investment instruction into a key value aiming at each risk control index, and matching a computing node for the risk control index according to the key value;
acquiring a position taken value of a computing node, and accumulating the position taken value and a computing field of the risk control index to obtain a computing result of the risk control index;
and summarizing the calculation results of the risk control indexes, and taking the summarized results as the wind control management standard of the investment instruction.
5. The cloud platform-based asset management method of claim 4, further comprising:
setting the calculation result of the risk control index as sample data, setting the number of input layer nodes of the BP neural network model according to the number of the sample data, and setting the number of output layer nodes of the BP neural network model as one;
determining the number of hidden layer nodes of the BP neural network model by adopting a trial-and-error method according to the number of the input layer nodes and the number of the output layer nodes;
inputting the sample data into an input layer utilizing the BP neural network model, completing the learning of the sample data through a hidden layer and an output layer in sequence, and taking the output result of the output layer as the evaluation result of the sample data.
6. The cloud platform based asset management method according to claim 3, wherein said performing, for said real estate asset data, operation management, balance management and asset management on said real estate asset data by using said real estate management platform comprises:
selecting a corresponding risk evaluation index according to the real estate information corresponding to the real estate asset data;
according to the risk evaluation indexes, an evaluation index judgment matrix is constructed, and consistency check is carried out on the evaluation index judgment matrix;
calculating a weight vector of the risk evaluation index through the evaluation index judgment matrix;
and calculating the risk integral of the real estate asset data by using the weight vector, and taking the risk integral as the value evaluation standard of the real estate asset data so as to finish asset management.
7. The cloud platform-based asset management method according to claim 1, wherein the storing and backing up of the asset data by using a data migration technology based on the full-period digital management result comprises:
receiving a data migration instruction, and performing accuracy test and verification test on the asset data to be migrated according to the data migration instruction;
after the accuracy test and the verification test are finished, packaging the asset data into a data stream;
and transmitting the data stream to a target server in real time through a protocol service, and analyzing the data stream by the target server to obtain the asset data to be migrated.
8. An asset management device based on a cloud platform, comprising:
the data storage unit is used for acquiring asset data and storing the asset data in a preset database based on a cloud platform technology, wherein the asset data comprises stock right asset data and real estate asset data;
the data management unit is used for respectively carrying out full-period digital management on the equity asset data and the real estate asset data in the database;
and the data backup unit is used for storing and backing up the asset data by using a data migration technology based on the full-period digital management result.
9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the cloud platform based asset management method of any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, implements the cloud platform based asset management method of any of claims 1 to 7.
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| CN202210633522.XA CN114896226A (en) | 2022-06-06 | 2022-06-06 | Asset management method and device based on cloud platform and related medium |
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